Overhead dimensioning system and method

ABSTRACT

A system and method for dimensioning large or palletized freight of one or more pieces determines the dimensions of a rectangular prism having the smallest volume which would contain the freight. The system is capable of being positioned remotely from the freight. The system is further configured to determine the dimensions in varying levels of ambient light and varying object surface reflectance. The system still further is configured to first rapidly scan an area to determine the general location of the boundaries of the freight and then more precisely scan the determined general boundaries of the freight to determine the precise boundaries of the freight.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Applicationentitled “Overhead Dimensioning System,” Ser. No. 60/302,509, filed Jun.29, 2001, the contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a machine vision system fordimensioning large or palletized freight of one or more pieces.

2. State of the Art

Systems for visually dimensioning objects are generally well known. See,for example, U.S. Pat. Nos. 4,731,853; 5,193,120; 4,929,843; 5,280,542;and 5,555,090, and “Optical Three-Dimensional Sensing for MachineVision,” T. C. Strand, Optical Engineering, Vol. 24, No. 1, pp. 33-40.Such systems scan the object and the surrounding surface with a laser,and detect the laser reflected off of the scanned object, as well as offthe surrounding surface, with a CCD camera. The detected laser image isanalyzed to determine the dimensions of the object via triangulation.

Such systems for dimensioning objects have required a level ofenvironmental structuring that has limited the potential application ofautomated dimensioning. In particular, such systems have been limited tosubstantially cuboidal objects and/or objects in known positions and,thus, have been unable to tolerate objects having highly noncuboidalshapes. Most often, limitations on the range of object size in relationto measurement resolution/accuracy have been imposed. In operation,these systems have been slow or awkward to use. Generally, the systemshave been intolerant to variations in object reflectance, within orbetween objects, and/or ambient lighting. In order to reduce occlusion,such systems typically utilize movement, i.e., rotation, of the objectand/or the sensing device, or require optical components to be locatedat the level of the object rather than being positioned remotelyoverhead. Finally, the common dimensioning systems have required costlyhardware. The present invention is provided to solve these and otherproblems.

BRIEF SUMMARY OF THE INVENTION

The present invention includes a method for determining the dimensionsof an item placed within a measurement space of a dimensioning systemand a measuring system and a computer-readable medium configured toimplement the inventive method. The components of the dimensioningsystem may be mounted remotely overhead and are configured to minimizeocclusion to recover the true dimensions of the object. The methodincludes scanning a signal through a measurement space. The acquiredimages are optically filtered and differenced to isolate the signal. Afirst laser and a first camera are utilized to determine an approximatelocation and dimension of the item. A second laser and a second cameraare utilized to determine an approximate location and dimension of theitem. A first set of point cloud data is acquired wherein the firstlaser scans a first signal through the measurement space and the firstcamera receives the reflections of the first signal. A second set ofpoint cloud data is acquired wherein the second laser scans a secondsignal through the measurement space and the second camera receives thereflections of the second signal. A third set of point cloud data isacquired wherein the first laser scans the first signal through themeasurement space and the second camera receives the reflections of thefirst signal. A fourth set of point cloud data is acquired wherein thesecond laser scans the second signal through the measurement space andthe first camera receives the reflections of the second signal. An imageis constructed by merging the first, second, third and fourth sets ofacquired point cloud data. A smallest rectangular prism, e.g., cuboid,rectangular parallelepiped, is determined to contain the constructedimage.

A further aspect of the present invention includes utilizing an imagepoint connection factor. The image point correction factor is determinedduring calibration of the dimensioning system and includes a set ofgenerated equations or lookup tables to correct lens distortion. Thedistortion corrections are utilized in cooperation with the constructedimage to determine the cuboid.

Yet a further aspect of the present invention incorporates a rapidscanning technique wherein a first, coarse, scan quickly locates anobject and, once located, a second, fine, scan is utilized to dimensionthe object. Alternatively, an adaptive scanning technique is utilizedusing both coarse and fine scans to locate and dimension the object.

Yet another aspect of the present invention includes acquiringadditional sets of point cloud data and constructing the image bymerging all the sets of acquired point cloud data.

One advantage of the present invention includes providing a dimensioningsystem wherein the working component(s) of the system are mountedremotely, e.g. overhead, to allow unobstructed passage throughout themeasurement space.

Another advantage of the present invention includes providing a systemfor dimensioning large or palletized freight of one or more pieces.

Yet another advantage of the present invention includes providing adimensioning system capable of being installed within existingoperational environments.

In accordance with the present invention. the system can determine thedimensions of a rectangular prism having the smallest volume but whichwould contain the freight.

In further accordance with the present invention, the system candetermine the dimensions in varying levels of ambient light and varyingobject surface reflectance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of hardware in accordance with the invention;

FIG. 2 is a flow chart illustrating basic steps performed by thehardware of FIG. 1;

FIG. 3 is a more detailed flow chart of one of the steps of FIG. 2;

FIG. 4 is a more detailed flow chart of another one of the steps of FIG.2;

FIG. 5 is a more detailed flow chart of still another one of the stepsof FIG. 2;

FIG. 6 is a more detailed flow chart of still another one of the stepsof FIG. 2;

FIG. 7 is a block diagram of another embodiment of the presentinvention;

FIG. 8 is an image of a box with a line of light from a projector;

FIG. 9 is a thresholded image of the box of FIG. 8;

FIGS. 10 a and 10 b are perspective drawings showing one embodiment ofthe present invention;

FIG. 11 shows a perspective projection in which object points areprojected through the image or view plane to a point known as the centerof projection or focal point;

FIG. 12 shows a schematic representation of the optical geometry used inthe method of stereo triangulation;

FIG. 13 is a block diagram showing the primary image processing stagesof one embodiment of the present invention;

FIG. 14 is a schematic side view of one embodiment of the presentinvention;

FIG. 15 is a schematic drawing showing geometric detail of the triangleformed by one embodiment of the present invention;

FIG. 16 is a schematic drawing of light from the box passing through thecamera lens and impinging on the CCD camera detection surface;

FIG. 17 is a block diagram of another embodiment of the presentinvention;

FIG. 18 is a block diagram of another embodiment of the presentinvention;

FIG. 19 is a block diagram of another embodiment of the presentinvention;

FIG. 20 depicts an undistorted image of a square;

FIG. 21 depicts a simulated image affected by radial lens distortion;

FIG. 22 depicts a simulated image affected by radial lens distortion;

FIG. 23 depicts an image on a coordinate frame;

FIG. 24 is a distorted image of a square;

FIG. 25 is a distorted image of a square;

FIG. 26 is a distorted image of a square;

FIG. 27 depicts a screen of a graphical interface of the dimensioningsystem of the present invention;

FIG. 28 depicts a screen of a graphical interface of the dimensioningsystem of the present invention;

FIG. 29 is a perspective drawing of one embodiment of the hardwareconfiguration of the present invention;

FIG. 30 is a schematic diagram showing the accumulated laser scan linesof a noncuboid object;

FIG. 31 is a block diagram depicting one method of determining a minimumenclosing rectangle;

FIG. 32 is a block diagram depicting another method of determining aminimum enclosing rectangle;

FIG. 33 is a front view of one embodiment of the dimensioning system ofthe present invention;

FIG. 34 is a front view of another embodiment of the present inventionshown in FIG. 33;

FIG. 35 is a top view of one embodiment of the dimensioning system ofthe present invention; and

FIG. 36 is a top view of one embodiment of the dimensioning system ofthe present invention.

DETAILED DESCRIPTION OF THE INVENTION

While this invention is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail a currently preferred embodiment of the invention with theunderstanding that the present disclosure is to be considered as anexemplification of the principles of the invention and is not intendedto limit the broad aspect of the invention to the embodimentillustrated.

One embodiment of a dimensioning system 10 of the present invention isillustrated in FIG. 1. The system 10 includes a CCD camera 12, sensitiveto long wavelength visible light (680 nm) having a lens 14 with an autoiris (iris controlled from computer and/or camera video signal).Alternatively, rather than an auto iris, a software approach could beused, wherein the camera integration time, i.e., shutter speed, isvaried, which could permit faster operation. The system 10 furtherincludes an infrared blocking filter 16 and a colored glass filter 18.The colored glass filter 18 passes deep red. The system 10 also includesa laser 22, having a 680 nm, 0.91 mW output, class II. The laser 22produces a “flat-top” line of light with a 60° fan angle. The laser 22is powered by a 5 V DC power supply 24. A mirror 26 is incrementallyrotated by a scanner 28 under the control of a motor drive 30.Specifically, the rotational position of the mirror 26 is proportionalto the voltage input to the scanner 28. A personal computer 32,incorporating input/output cards (not shown), controls rotation of themirror 26 and operation of the laser 22, as well as performing othercalculations, discussed below. The personal computer 32 controls thelaser 22 via a TTL signal 33. The laser 22 forms a plane of light,generally designated 34, upon an object 36 to be measured. The object 36can be one or more pieces. The object 36 is located on a measurementspace of a surface 38, which may be a pallet, or directly upon a floorsurface.

The general steps performed by the system 10 are illustrated in FIG. 2.As discussed in greater detail below, the system 10 performs twoscanning steps to dimension the object 36. The first scanning step is acoarse scan, wherein the mirror is incremented in relatively largeincrements, to coarsely determine the location of the start point andend point of the object 36. The second step is a fine scan, wherein theminor 26 is incremented in relatively small increments near the startpoint and end point of the object 36 to precisely determine the locationof the periphery of the object 36.

Preferably, in a Find step, the object 36 is scanned by the laser 22 inrelatively coarse steps to determine whether an object 36 is presentand, if so, the general location of the beginning and ending of theobject 36. If an object is not present, the system 10 stops. However, ifan object 36 is present, an Acquire step is performed, wherein theobject 36 is re-scanned by the laser 22, but in relatively fine steps.

An alternative scanning technique, intelligent scanning, cansignificantly reduce the amount of time to dimension a single object.Intelligent scanning begins with a coarse scan at a location off-centerof the measurement space wherein the object rests. The coarse scancontinues in a first direction, e.g., forward, until an object is foundor until it is determined that there is no object near the center of themeasurement space. If an object is found, the coarse scan is continuedin the first direction until an edge is found. The fine scan is theninitiated in a second direction opposite to the first direction, e.g.,backward, over the edge. The coarse scan is then resumed at the initialstarting point in the second direction until the object's other edge isfound, wherein the fine scan is initiated in the first direction uponlocation of a second edge. If the object is not found with the firstscan signal but the object edge is found with the subsequent coarse scansignal, the fine scan of the edge is immediately performed. Then, thecoarse scan is resumed to find the other edge, wherein the fine scan issubsequently initiated.

A Perspective step is then performed, which adjusts the length (“x”) andwidth (“y”) dimensions in view of the height (“z”) dimension. This isbecause small objects close to the lens appear the same as large objectsdistant from the lens. A Cube function is then performed whichdetermines the dimensions of a rectangular prism having the smallestvolume about the object 36.

The Find step (coarse scan) is illustrated in greater detail in FIG. 3.In a first step, the system is initialized, and then a first image isobtained by the camera 12. The input voltage to the scanner 28 isincreased by a coarse step, e.g., 0.4 V, which advances the mirror 26 arelatively large increment. The first image is electronically stored anda second image is obtained. In order to eliminate adverse effects ofambient light, the first image is subtracted from the second image,which eliminates the ambient light factor, leaving only the laserportion. This gray level image is then utilized as a threshold toprovide a binary image.

Since the color and reflectivity of objects being measured vary, thesignal may overrun into adjacent pixels, causing some measurementinaccuracies. Some of these inaccuracies may be addressed by athresholding operation or by subsequent image filtering. Also, noise maybe more prevalent in light-colored, shiny objects. For instance, forlight-colored, shiny objects, the laser signal reflection is bright,and, conversely, for flat, dark-colored objects, the laser reflectionsignal is significantly smaller. Consequently, the optimum binarydecision threshold to be used needs to be adaptive according to thereflectance/coloring of the object. It may also be necessary toadaptively alter either the camera aperture or camera integration time.Such “automatic” thresholding occurs when an object is found during ascan and the gray-scale values of the points found in the image above athreshold are gathered. A statistical property value, e.g., mean, ofthese points is used to choose one of a predetermined set of thresholdvalues, preferably a set of three. The three threshold values and thescan determination values are determined during a calibration phase ofthe system. To further increase the coarse scan speed, every fifth pixelof the threshold result is searched to locate the highest pixel, andthen the height of the highest pixel is determined. The presentdisclosure assumes the object has a minimum programmable height and maybe located on a pallet of minimum programmable height, e.g. 8 cm high.Therefore, the object itself will always have a height greater than 8cm. The system 10 can separate the object 36 from the pallet based uponits height. It is also possible for the system to automaticallydetermine the height of the pallet.

The purpose of the Find function is to establish the position of thelaser 22, measured in volts, both at the point at which the laser first,i.e., “start,” and last, i.e., “stop,” encounters the object 36. Thelower box in FIG. 3 works as follows. At the start of the program,“startflag” is initialized to 0. The input voltage to the scanner 28 isincrementally increased (by 0.4 V increments) within the loop. If anobject greater than 8 cm in height is encountered while “startflag”equals zero, then “start” is set to volts and “startflag” is set to one.By changing “startflag” to equal one, “start” will not be altered duringsubsequent passes through the loop. The second “if” statement in thefinal block states that if height is greater than 8 cm, then set “stop”to volts. Thus, for subsequent passes through the loop, “stop” maycontinually be reset, i.e., if height>8 cm. Therefore, at the end of thelaser scan, “start” and “stop” are set to the points at which the laser22 first and last encountered the object 36, respectively.

The Acquire function is illustrated in FIG. 4. This function is similarto the Find function, except that the mirror 26 is incremented inrelatively small steps at the start point and end point of the object36. Additionally, the height of every pixel, not every fifth pixel as inthe Find function, is calculated and stored. Additionally, dependingupon the quality of the lens (short focal length lenses have greaterperipheral distortion), peripheral correction can also be conducted. Ina final step, data, e.g., a three-dimensional cloud of points, having aheight greater than 8 cm, to distinguish the object 36 from the pallet,is formed.

The next step is the Perspective function and is illustrated in FIG. 5.In this function, the personal computer increments through the storedcloud of data points and converts the “x” and “y” values from pixels tocentimeters. Based upon conventional equations, these converted valuesare then adjusted, based upon their respective “z” value and stored.

The next step is the Cube function, illustrated in FIG. 6, whichdetermines the dimensions of a rectangular prism having the smallestvolume about the object 36. The rectangular prism will always have abase parallel to the pallet, or other surface on which the object 36rests. In a first step, the cloud of data points is rotated about thez-axis to determine a rectangle having the minimum area but whichencloses all of the “x” and “y” coordinates. The cloud of data pointscontinues to rotate a total of 180° to determine the smallest rectangle.This determines the length and width, e.g., breadth, of the rectangularprism. The system 10 then determines the largest “z” value, which is theheight of the rectangular prism.

Utilizing the plane of light, e.g., a laser line, provides advantages interms of being resistant to the effects of changes in backgroundlighting, or the presence of labels and other albedo patterns on theobject 36. This ability may also be enhanced by placing a filter overthe camera, which is opaque to all frequencies of light other than thatof the laser. The scanning line can further enable detection of morecomplex morphologies, which is useful for objects other than cuboids.

FIG. 7 depicts a dimensioning system 10 including a CCD camera 12mounted above an object 36, e.g., cuboidal box, wherein a light-strip(laser) projects diagonally onto the box to produce a “pulse” type ofimage. The trigonometry of the system in cooperation with the image canbe analyzed to determine the dimensions of the object 36. The camera 12is mounted at a predetermined distance from the object 36 and capturesthe image shown in FIG. 8 by receiving a signal from the laser 22. Thisimage was captured in the presence of a typical background, e.g.,variable daylight. A filter in combination with a predeterminedfrequency of laser light can be utilized to effectively remove anydetrimental background lighting effects. The filter is mounted on thecamera and is generally opaque while transmitting the laser frequency.Such a technique can provide a significant benefit of being able tooperate in typical ambient lighting. The image depicted in FIG. 8 wasthresholded and is shown in FIG. 9.

Small noise elements in the measuring field can cause large errors inthe dimensioning process. The noise may be attributable to small debrisobjects within the field of view or specular reflections of the laser onthe measuring surface. To remove visible noise from the image, medianfiltering can be applied to the image. Median filtering is consideredappropriate when the aim is to reduce noise while preserving edges. Eachpixel is set to the median of the pixel values in the neighborhood ofthe pixel, e.g., 4×4. During image measurement applications, edges areoften more useful than regions. Therefore, the image can be subjected tofurther filtering that will result in an increased emphasis on theedges. FIG. 9 more clearly shows the “pulse” referred to earlier. Theheight of the pulse can be used to determine the height of the object36. For example, in FIG. 7, the height of the camera above the table is160 cm, and the horizontal distance from the projector to the camera is112 cm. The angle between the light strip and the floor, Θ, is 55°.Therefore.H=d·tan(1)  Equation 1where d is the apparent height of the pulse shown in FIG. 14 (referredto as the line separation), and H is the height of the object 36. Theline separation can be determined by using the following procedure:

-   -   calculate the center of mass (COM or centroid) for the image;    -   calculate the average y-value for the pixels above the COM;    -   calculate the average y-value for the pixels below the COM; and    -   subtract the first y-value from the second to obtain the line of        separation.

The above procedure was employed in a MATLAB function and the lineseparation was found to be 146.3 cm. The length of a line on the floorwas measured and compared to its length in the image in terns of pixels,and it was found that one pixel corresponds to 0.04774 cm. Consequently,the line separation was found to be 6.98 cm. Utilizing this value, H isdetermined to be 9.98 cm. Since the measured value for H is 10.1 cm, thecalculated object height has an accuracy of 98.8%.

The present invention is capable of incorporating several additionalnoise detectors, filters, and methods that can be implemented to findand eliminate noise during the dimensioning process. A further noisedetection method computes a spatial histogram of a point cloud dataimage in the horizontal and vertical directions. Spatially connectedvalues in the histogram or, in the case of the readings along thevertical axis, values with minimal gapping, are considered to be anobject. Groups of spatially detached values in any of the histograms aredetermined to be noise or another object. If the total number of pointsin the secondary object is less than a predetermined threshold, then thepoints associated with that secondary object are considered to be noiseand are removed from the point cloud data.

Further noise reduction can be accomplished by utilizing additionalvertical and horizontal histograms of an array, or image. Multiplerotations can be incorporated at varying increments, e.g., 30°, 45°,etc., wherein the array is rotated in space in the x and y planes.

Another noise detection method utilizes each column of each measurementimage to identify a position of each disjoint point in the column. Ifmore than one signal is found in each column, one of the points can beassumed to be noise. When more than one signal is found in a givencolumn, the height values of the multiple signals are compared with theheight values of other signals in the surrounding spatial area. Thesignal point(s) that most closely matches those in the nearby area isconsidered as part of the object.

Yet another noise detection method sorts the heights of the points inthe object cloud. The spacing between the points is evaluated and pointsof similar height are grouped together. If any one group has a verysmall number of points, these points are eliminated from the objectpoint cloud.

Another embodiment of the present invention for the determination of theheight, length, and breadth of a cuboid utilizes the method ofstereopsis. This method can be used in conjunction with other methodsdescribed in the multiple camera configuration. The system comprises twoidentical square pixel (11×11 mm) gray-scale cameras fitted with 8 mm(focal length) lenses. The cameras are positioned to view an objectvertically from above, as shown in FIGS. 10 a and 10 b. The separationbetween the camera centers can vary in the range of 4.5 cm to 58 cm, andstill larger spacing can be attained, e.g., 6 ft., with the camerasangled inward. The camera optical axes are parallel and perpendicular toa baseline connecting the two cameras, and the lens optical centers areapproximately 116 cm above a level surface. The surface is preferablylight-gray in color. Images of 768×576 pixels at 256 gray levels areacquired using an IMAQ 1408 framegrabber card. The object may beilluminated using two 500 W halogen lamps positioned near the cameras.

Generally, two classes of projection are considered in planar geometricprojection—perspective and parallel or orthographic projection. In thecase of perspective projection, distant objects appear smaller thanthose nearby and are characterized by a point known as the center ofprojection. FIG. 11 shows a perspective projection in which objectpoints are projected through the image or view plane to a point known asthe center of projection or focal point. The location of the projectedpoint on the image plane is given by:u=(f/(z+d))x v=(f/(z+d))y  Equation 2

In parallel or orthographic projection, the lines of projected rays areassumed to be parallel, where the location of the projected point on theimage plane is given by:u=x v=y  Equation 3

Stereopsis, binocular stereo, and photogrammmetry all refer to a processof judging distance by observing feature differences between two or moreimages usually taken from different locations under similar lightingconditions. To interpret a stereo pair, it is necessary to recover atransformation between the two camera coordinate systems.

FIG. 12 shows a schematic representation of the optical geometry used inthe method of stereo triangulation. The distance, or range, of an imagefeature from the view plane may be determined from the correspondinglocations of any projected feature, e.g., the projected laser line,within the respective image planes of the two parallel cameras. Assumingthe camera spacing (d) and camera focal lengths (f) to be fixed, thedistance to the feature may be derived (using similar triangles) from,z=df/(u _(L) −u _(r))  Equation 4wherein the term (u_(L)−u_(r)) is referred to as the image disparity.From Equation 4, it can be readily observed that:

-   -   the distance (z) is inversely proportional to the disparity; the        distance to near objects can, therefore, be measured accurately,        while the distance to far-off objects cannot;    -   the disparity is directly proportional to the separation of the        cameras, d; hence, given a fixed error in determining the        disparity, the accuracy of z (depth) determination increases        with increasing d; and    -   the disparity is proportional to the lens focal length,f;this is        because image magnification increases with an increase in focal        length.

From the above, it is clear that the greater the camera separation (d),the greater the disparity and the better the accuracy in thedetermination of z. However, as the separation of the cameras increases,the two images become less similar. This is sometimes known aswide-angle stereo; i.e., there is likely to be less overlap between thetwo fields of view. For example, some objects imaged by one camera maynot be visible to the other. This leads to a breakdown in the method.Also, it is more difficult to establish correspondence between imagepoints in wide-angle stereo. The difficulty in applying stereotriangulation arises in reliably determining the corresponding featureswithin the two separate images. The key to an automated stereo system isa method for determining which point in one image corresponds to a givenpoint in another image.

Utilizing an invariant moment analysis method for determining anobject's length and breadth, the ratio of the object's principal axesmay be derived. If the object is assumed to be a cuboid, then the lengthand breadth (in addition to the location of the centroid and theorientation of the principal axis) can be determined in units of pixels.To express these dimensions in real world units, e.g., cm, it isnecessary to calibrate the system, that is, to establish the size of animage pixel in world units. For an object at a fixed distance, this mayreadily be done by first acquiring an image of a similar object of knownsize. However, in the current application, the distance to the top ofthe cuboid object is a variable, which is dependent upon the object'sheight. Thus, two cuboid objects of equal length and breadth, butdiffering height, can appear to differ in all three dimensions. It is,therefore, necessary to introduce a calibration factor in terms of thevariable z:

-   -   calibrated dimension=pixel dimension * (pixel size * range        (z)/lens focal length (f))

Since the fixed position of the cameras is known, the object height maybe determined using Equation 4. To achieve this, it is necessary tosolve the correspondence problem, i.e., to find an object feature, ormore specifically an object point, that is visible in both cameraimages. This pair of image points is sometimes known as a conjugatepair. Several techniques have been reported in the scientific literaturefor undertaking this task, including correlation methods, gray-levelmatching, and edge-based methods. One solution is to utilize theprojected laser in each view to form the conjugate pair.

As shown in FIG. 13, the primary image processing stages are:acquisition, i.e., the capture of stereo gray-level images;preprocessing, i.e., convolution filtering to improve edge definition,etc.; blob, e.g., object, sedimentation, i.e., using a fixed or adaptivethreshold; and feature extraction. i.e., determination of principaldimensions.

The feature extraction stage includes the determination of object heightin world coordinates, e.g., cm; length and breadth in image coordinates,e.g., pixels; and length and breadth in calibrated world coordinates,e.g., cm.

To further understand the present invention, the results and analysis ofa method utilizing scanning laser light and vision system techniques fordetermining the height of a cuboidal object is presented. It is to beunderstood that the present invention is not to be limited to theseresults and analysis. A geometrical analysis was performed to allow forparallax and perspective effects. The technique produced accurate heightvalues. For boxes placed directly under the camera, errors in themeasurements were less than the variation in height across the width ofthe box. For example, an 18 cm-height box was moved by 50 cm in the xand y directions, and the corresponding height value was 17.9 cm.Therefore, for this analysis, maximum errors in height determinationwere less than +/−1%.

The system comprised a laser apparatus having a Class II laser diode(635 nm) with a cylindrical lens producing a plane of light with a fulldivergence angle of 60° and a precision scanner with a mounted mirrorutilizing drive electronics tuned to the mirror. The orientation andlocation of the scanner and mirror can be adjusted as required for theapplication. Also included in the system were instrumentation andcontrol apparatus including an input/output card, framegrabber card,cabling, and connections. The software included LabVIEW 6I with NI-DAQsoftware (used for controlling the mirror) and IMAQ software (for imageacquisition and analysis). Additional equipment comprised: 512×512gray-scale camera (pixels 11 micron×11 micron), HP Vectra PC, andcuboidal boxes of various dimensions. The measurement surface on whichthe boxes were placed was painted matte black.

The configuration of the system is shown in FIG. 14 wherein Hm=190 cm,Hc=139 cm, and Lo=116 cm. Three possible locations for an object areshown in FIG. 14. A geometrical analysis was performed for the twogeneral cases shown, i.e., placement of the object 36 at position 1 andat position 2. FIG. 14 is a schematic side view of the experimentalarrangement of the mirror, camera, and box (shown at three possiblelocations). Many of the angles and dimensions that need to be found forthe determination of box height are shown in FIG. 14. FIG. 15 showsdetail of the triangle formed by the camera and the point at which thelaser light impinges on the box and on the surface. For this triangle,the Sine Rule states,d/sin (D)=i/sin (I)d=i·sin (D)/sin (I)Since the sum of the internal angles for a triangle is 180°,I=180−D−(A+E)Also, from the Theorem of Pythagoras,i=((L1)²+(Hc)²)^(0.5) d=((L1)²+(Hc)²)^(0.5))sin(D)/sin(180−D−(A+E))

It can also be seen from FIG. 15 that,cos(A)=he1/dhe1=d·cos(A)Therefore,he1=((L1)²+(Hc)²)^(0.5))sin(D)·cos(A)/sin(180−D−A−E)  Equation 5Equation 5 can be used when the horizontal distance from the mirror tothe box is less than Lo. Similarly, for a box positioned at position 2,he2=((L3)²+(Hc)²)^(0.5))sin(H)cos(C)/sin(180−H−C+G))  Eqation 6Equation 6 can be used when the horizontal distance from the mirror tothe box is greater than Lo.

Equations 5 and 6 can, therefore, be used to determine the height of abox, assuming that the laser light can be seen as it impinges on the topof the box and on the surface. This would be seen at the camera as twolines of light.

Further, due to the uncertainty as to the color and texture of thesurface that is utilized in the warehouse environment, it is desirablethat the height of the box could be determined without the need todetect the laser light as it impinges on the adjacent floor surface ofthe measurement space. Black rubber matting has a tendency to reflect aminimal proportion of the incident light so that good imaging of theline may not be possible. It is further desirable that the height of theobject be determined purely from analysis of the line of laser lightvisible on the top of the object. This can be achieved due to the highlevel of accuracy and repeatability attainable from the scanner that isused for positioning the mirror. The rotational position of the mirroris proportional to the voltage supplied to the scanner's driveelectronics. LabVIEW software is utilized to supply a number of voltagesand the corresponding position of the laser line on the table can bemeasured. Trigonometry is used to relate this to the angle of themirror, A. Solving the resulting simultaneous equations allows for theangle of the mirror to be calibrated in terms of applied voltage using,for example, the following equation:A=1.964(V)+17.94  Equation 7where V is the applied volts.

For a given voltage applied to the scanner, it is possible to predictthe position of the laser line on the floor surface. This position isquantified in terms of the y-pixel coordinates of the centroid of theline, as viewed at the camera. The camera was arranged such thaty-coordinate values increased as the line moved to the left side, asshown in FIG. 14. This pixel value does not vary linearly with the angleof the mirror, A; however, it may be expected to be proportional totan(A). Therefore, the mirror can be positioned to various angles,noting the corresponding pixel values. Solving the simultaneousequations yields the following:pixel y-value=−1020.43(tan(A))+883.32  Equation 8

Most of the values in Equation 5 needed to calculate the height areavailable wherein L1 can be found from the geometry shown in FIG. 14;the equation is:L 1 =Lo−Hm(tan(A))

The determination of the angle D, which is the angle subtended at thecamera lens by the pixel y-value of the laser line that impinges on thefloor surface (determined using Equations 7 and 8 for a given voltage.V, and the pixel y-value of the laser line that impinges on the top ofthe box (found from analysis of the image)) can be through analysis ofthe paths of light that pass through the camera lens and impinge uponthe charge coupled array. This is shown in FIG. 16 for detection of theheight of the box at position 1. From FIG. 16.q=y 1 −y0where y1 is the predicted y pixel value for the laser light whichimpinged on the floor surface, and y0 is the y value of the centralpixel. i.e., 256.

Also,q+r=y 2 −y0where y2 is the y-pixel value for the line on the top of the box. Asexplained above, y0, y1, and y2 can be found, and, therefore, q and rcan be determined, p is the focal length of the lens, e.g., p=8 mm.Therefore, t can be found from the Theorem of Pythagoras. The CosineRule states,cos(D)=(t ² +s ² −r ²)/2ts  Equation 9The above formula provides for determining the angle D. This can then becombined with the other derived parameters and substituted into Equation5 to give the height of the box, he1.

In one example, Hc, the height of the camera above the table, is 139 cm.Hm is the height of the scanner mirror above the table and is 190 cm. Lois 116 cm and is the orthogonal distance from the scanner mirror to thetable. A voltage of 4.9 V applied to the scanner driver provides themirror an angle A of 27.56°. E was determined to be 6.9° and L1 to be16.83 cm. A box was placed on the surface and the measured value for y2(the y pixel of the centroid of the line on top of the box) was found tobe 389.8. The value for y1 (the predicted y value for the centroid ofthe line on the floor) was 350.71. The value for y0, the center pixel inthe camera's field of view, is 256.

$\begin{matrix}{q = {{y1} - {y0}}} \\{= {350.71 - 256}} \\{= {94.7\mspace{14mu}{pixels}}} \\{{thus},{q = {1.04\mspace{14mu}{mm}\mspace{14mu}\left( {1\mspace{14mu}{pixel}\mspace{14mu}{has}\mspace{14mu} a\mspace{14mu}{side}\mspace{14mu}{length}\mspace{14mu}{of}\mspace{14mu} 11\mspace{14mu}{microns}} \right)}}} \\{{q + r} = {{y2} - {y0}}} \\{= {389.8 - 256}} \\{= {133.8\mspace{14mu}{pixels}}} \\{= {1.4718\mspace{14mu}{mm}}}\end{matrix}$Therefore, r=0.43 mm. p, q, and r can be used to find t and s:t=(p ² +q ²)^(0.5) (p is the focal length of the lens, e.g., 8 mm.)thus, t=8.067 mms=(p ²+(q+r)²)^(0.5)thus, s=8.134 mmEntering these values into Equation 9 yields a value for angle D of3.005°. By substituting this value into Equation 5, along with the othervalues given above, the value of he1 was determined to be 10.67 cm. Themeasured height of the box was found to be 10.6 cm.

An accuracy check of the laser line method for height measurements of abox at a significantly different point in the field of view reveals thata change in the position of the box in the camera's field of view hasany significant effect on the accuracy with which the height can bedetermined using the scanning laser line technique. Again, using the 8mm lens, a box was placed at a displacement of 40 cm in both x and ydirections from the camera. The line of light impinged on the top of thebox when a voltage of 3.9 V was applied to the scanner driver.Calculations showed that A=25.6°, L1=24.97 cm, D=5.3797°, and E=10.18°.From these values, he1 was determined to be 17.9 cm. This compares witha height value from direct measurement with a rule of 18 cm, giving anerror of 0.55%.

The line scanning technique described here offers a number of advantagesin addition to high accuracy height measurement. For example, imageanalysis is simplified since, at any given time, the image captured issimply that of the line section which is impinging on the top of thebox, and the orientation of this line relative to the camera does notchange. A combination of such images (for different mirror angles) canbe used to determine the length, width, and location of the box, asdescribed earlier. Due to the large amount of information providedduring the scan, the technique also offers potential for quantificationof the morphology of more complexly shaped objects.

Various techniques can be implemented to reduce the scanning time andamount of memory typically required in dimensioning systems. Some ofthese techniques include a quick scan of each image to determine if anyobject, i.e., line segment, is present. If not, then that image would beimmediately discarded. Also, coarse scanning of a plane of light couldbe utilized for position detection, followed by finer scanning fordetermination of the four sides of the object. The measurement densityrequired will depend upon the resolution required from the system. Forexample, if the smallest object that the system needs to detect is acube of a side length 30 cm, then it would not be necessary to scan theline across the floor in intervals of less than approximately 25 cm. Iffurther reductions in time are required, conventional image processingcould be combined with line scanning. The direct image processing mightquickly give the centroid of the plan view of the box (along with itslength and width). The laser line would be directed to the imagecentroid and then scanned until an image of the line on top of the boxwas attained. Processing of this one image would then give the boxheight. Such a system may Generally be expected to allow determinationof the box parameters in a very short time, e.g., less than one second.

Perhaps one of the more formidable difficulties to be overcome in visionsystem box measurement is associated with thresholding and field ofview. By means of adjusting the camera aperture or integration time, andapplication of a suitable threshold, it is possible to obtain imagesconsisting of only the laser line as it passed over the top of theobject. However, when the intensity of the background light increasesother features become visible, such as reflections of daylight from thefloor and from plastic tape present on the boxes. These effects can beavoided by utilizing an infrared laser with a filter placed on thecamera lens so that only the laser light would be visible to the CCDarray.

The active nature of the structured lighting approach has significantadvantages over more passive lighting techniques, particularly givenpossible complexity in object shape and the already relativelyunstructured nature of the environment, i.e., difficulty in controllingambient lighting and variation in object position and size. Shadowingproblems may be alleviated by moving the laser closer to the camera(with some reduction in accuracy) or simultaneously scanning fromopposing directions. FIG. 17 depicts this configuration, although deeprecesses will remain difficult to recover.

Alternatively, as shown in FIG. 18, stereo triangulation in cooperationwith a scanning laser mounted near the camera(s) can be utilized todetermine range. This configuration reduces the problem of shadows whileagain taking advantage of structured lighting to simplify the imageanalysis. It might be possible to determine object position, length, andwidth by initially using a single uniformly illuminated image togetherwith a method of moments, and then actively directing the laser tolocally scan across the object to recover the height profile using thetriangulation method. Such a system is a hybrid of both passive(relatively unstructured) and active (structured) illumination,attainable perhaps by utilizing a dual-image threshold.

Alternatively, when capable of segmenting the object by thresholding,determining the height of a more complex object is simplified byutilizing a second camera viewing the object horizontally. One suchconfiguration of a two-camera system is shown in FIG. 27. The secondcamera is mounted at approximately 60° from the horizontal. This type ofconfiguration may require a type of tomographic approach, or a form ofstereo, to find the dimensions of the object.

Another aspect of the present invention involves a simple method forcorrecting the location of image points when subject to radial lensdistortion. The approach requires only two calibration images toestablish the necessary distortion coefficients.

Given the desire to achieve a relatively compact overall working volumeof the dimensioning system 10, it may be preferable to view largeobjects at relatively close proximity. A wide angle of view may beachieved by using a lens of short focal length, e.g., less than 12 mm;however, this is at the cost of some image distortion, sometimes knownas “barrel distortion.” Radial lens distortion can be approximatedmathematically; however, as related by Schluns and Koschan, it becomesdifficult to reliably model the distortion given inevitable variationsin lens quality. An ideal model of lens distortion leads to an infinitenumber of distortion coefficients.

FIG. 20 depicts an undistorted image of a square and FIG. 21 depicts animage subject to considerable radial lens distortion in which thecorners of the distorted image are observed to be projected towards thecenter of the image. Notice also that the distortion can be reversed, inwhich case the comers of the image are now projected away from the imagecenter, as shown in FIG. 22.

A reasonable approximation of the lens distortion may be obtained byconsidering only two coefficients, C₁ and C₂. Consider a coordinateframe located at the center of the image shown in FIG. 23. Let x_(d) andy_(d) be the distorted image coordinates, and x_(u) and y_(u) be theundistorted image coordinates, for which:x _(u) =x _(d)(1+C ₁(x _(d) ² +y _(d) ²)+C ₂(x _(d) ² +y _(d) ²)²) and y_(u) =y _(d)(1+C ₁(x _(d) ² +y _(d) ²)+C ₂(x _(d) ² y _(d) ²)²)The distortion coefficients, C₁ and C₂, can be determined by suitablecalibration. If C₁ or C₂ are positive, then the image is projected intowards the center, and, conversely, if negative, the image is projectedout away from the image center.

To calculate C₁ and C₂, distorted images of two objects of differingsize are utilized. The objects are positioned at the center of the fieldof view. Given that the image distortion tends to increase towards theedge of the image, one of the objects is chosen to be quite large inrelation to the field of view. The distorted images of a square of 100pixels and 150 pixels are shown in FIGS. 24 and 25, respectively.Preferably, the objects are square-shaped so that the corner featuresmight readily be identified. The coordinate location of the top leftcorner of each square is measured, relative to the center of each image,and found to be (−45, 45) and (−60, 60), respectively, where thecorresponding undistorted coordinates are (−50, 50) and (−75, 75),respectively. (Image size 200×200 pixels with coordinate frame locatedat image center.) Thus,−50=−45(1+4050C ₁+16.4×10⁶ C ₂)and−75=−60(1+7200C ₁+51.84×10⁶ C ₂)Solving these simultaneous equations yields C₁=1.8×10⁻⁵ and C₂=2.3×10⁻⁹.Further,x _(u) =x _(d)(1+1.8×10⁻⁵(x _(d) ² +y _(d) ²)+2.3×10⁻⁹(x _(d) ²)²)andy _(u) =y _(d)(1+1.8×10³¹ ⁵(x _(d) ² +y _(d) ²)+2.3×10⁻⁹(x _(d) ² +y_(d) ²)²)For a distorted image of a square of 180 pixels shown in FIG. 26, themeasured x-coordinate of the upper left corner was found to be −67pixels. (image size 200×200 pixels, with coordinate frame located atimage center.) This gave a calculated undistorted location of −90.25pixels, which compares favorably with the actual undistorted location of−90 pixels.

This relatively simple approach provides a useful mechanism for thecorrection of the location of image points when subject to significantradial lens distortion. The distortion coefficients can be determinedduring calibration of the dimensioning system and stored in a lookuptable for access during the dimensioning process. Alternatively, usingaspherical lenses may also reduce the effects of “barrel” distortion.

Another alternative to correcting for the lens distortion is to createequations or lookup tables to compensate for the distortion. The lasersignal is scanned over the entire measuring region in very fineincrements. At each position of the laser, through mathematical modelingusing the known angle of the laser, relative camera and laser positions,and ideal lens properties, the theoretical placement of the signal onthe sensor array can be determined. Images are gathered at each laserposition by the camera. A comparison is made between the theoreticalvalue the pixel should have and the actual value detected during themeasurement. From the resulting data, a lookup table can be generatedthat indicates pixel correction values for each pixel.

An alternative method of removing distortions requires scanning themeasurement in relatively small, predetermined increments. Thex-coordinate field is segmented into multiple segments, e.g., 10. A meany-coordinate value is determined for each segment and each scan.Creating sets of (x, y) data where the x value represents the voltageincrement of the laser and the y-value represents the spatial y-positionof the laser in the image, polynomial line-fitting routines are used tocreate equations that describe a baseline voltage-laser relationship forthe image. This baseline measurement effectively provides informationthat, when compared with expected values, is used to remove distortions.

A graphical interface for a cuboidal and noncuboidal object dimensioningsystem is depicted in FIGS. 27 and 28, respectively. The cuboidal systemalso incorporates a second screen (not shown) that displays the scanninglaser line as it traverses across the object. The graphical window forthe noncuboidal system also displays the scanning laser line, as well asan image representing the entire scanned object surface, with asuperimposed minimum enclosing box.

FIG. 29 is an illustration of one embodiment of the dimensioning systemhardware. A frame constructed of aluminum supports the laser scanningunit and a camera. The laser control electronics and computer system,including I/O and framegrabber cards, are shown near the left side ofthe photograph.

Operation of the object measuring system is based upon the concepts andmethods described. For the noncuboidal system, the height of the objectis continuously determined during the laser scan of the object and then,on completion of the scan, the object's length and width are determined.In total, a cloud of 442,368 three-dimensional data points are typicallyacquired during a single scan. By calculating the object's height duringthe scan, it is possible to selectively remove low-lying points, oftenrepresenting a pallet, from the point cloud data. The dimensioningsystem incorporates a short focal length lens (6.5 mm) to allow objectsranging in size from 12 in.³ to 96 H×72 L×72 W in. to be measured usinga system height of only approximately 186 inches. The camera utilizes anarrow band interference filter to eliminate ambient light.

The system 10 was implemented by employing a program written usingNational Instrument's CV1 software, a C-based programming language thatincorporates specialized functions for data and image acquisition andprocessing. In determining the dimensions of a cubodial object, thedimensioning system utilizes a saw-tooth waveform generator (with asuitable I/O card) to produce an analog voltage. At specified intervals,the voltage is sent to the scanner electronics and used to drive themirror to a known position. A “framegrabber” is then used to grab animage using the CCD camera attached to the system. The capture of animage while the mirror (and therefore line) is momentarily stationaryreduces and/or eliminates any possible errors caused by movement. Theimage is subtracted from a previous image and then thresholded toproduce a binary image. The height of each point is calculated using thepreviously described methods. The points of all scans are combined intoa new image “cloud.”

During determination of the dimensions of a noncuboidal object, thedimensioning system continually calculates the height of allthree-dimensional pixel points during the laser sweep of the measuringvolume. This allows any background objects, such as a pallet or anymarkings on the floor, etc., to be removed from the cubing task. Forexample, the system may delete all pixels below 6 cm in height. As shownschematically in FIG. 30, the remaining pixels are accumulated to form athree-dimensional cloud of data points representing the surface of thescanned object(s). Object maximum and average height are calculatedduring the laser sweep. Object length and width are calculated byfitting a “minimum enclosing rectangle” to a plan view of the data pointcloud, as shown in FIG. 31.

Determination of the minimum enclosing rectangle is acquired by usingthe earlier-described techniques, see FIG. 6, in which the enclosingrectangle is effectively rotated through a series of angular increments,e.g., 3°, until the smallest, in terms of area, enclosing rectangle isfound. The smallest dimension, i.e., object width, and the dimensionperpendicular to this, i.e., object length, are found. Although theenclosing rectangle will have the smallest width, rectangle A in FIG.32, it may not have the smallest area. Alternatively, the solution maybe to find the enclosing rectangle with the smallest area, rectangle Bin FIG. 32.

The system 10 of the present invention is able to accurately determinethe height of any object. This is due to the geometrical analysis andcalculations that were performed to take into account the effects ofparallax and perspective.

In one embodiment of the present system, height data is continuouslycalculated and used to find the maximum and average height values duringthe laser scanning cycle. The maximum height is sensitive to disturbancefrom noisy outliers and may cause a reduction in measurement accuracy.Alternatively, the point cloud data can be accumulated and stored duringthe laser scan and then subsequently analyzed. A further advantageallows a three-dimensional cloud of data points to be displayed with theminimum-enclosing cube superimposed, offering better visualization ofthe cubing task. Outliers and noise can be more readily deleted from thebody of acquired data, possibly using global methods such as erosion anddilation. The duration of the scan could further be reduced by onlyconsidering pixels at or behind the advancing scan line, i.e., floorlevel. Time taken for the analysis of the data itself could also beimproved by only considering object edge or boundary pixels during thecubing of the point cloud data.

In general terms, the more distant the lasers and cameras from theobject, the greater the tendency toward orthographic project. While thishelps to reduce occlusion, the laser signal will tend to be reduced inintensity, and the system accuracy reduced. Similarly, positioning thelaser and camera units in close proximity will also tend to reduceocclusion, but at the cost of a reduction in system accuracy. Theseissues can be addressed by utilizing appropriate subsystemconfigurations of the present invention. FIG. 33 depicts an alternateembodiment of the dimensioning system's hardware configuration adoptedfor the reduced laser and camera occlusion, sometimes referred to asshadowing. This arrangement represents a compromise in terms ofminimizing camera and laser occlusion while simultaneously offeringreasonably optimized dimensional recovery accuracy when combined withthe lookup table calibration approach previously described. By locatingthe laser units outside the cameras, the lasers tend towards an idealcollimated source, helping to minimize possible occlusions off to oneside, i.e., away from the axis of the horizontal mounting rail.

In terms of hardware, there are now two subsystems, i.e., there are twocameras and two lasers. However, from an operational standpoint, thereare actually four subsystems available. Table 1 lists the hardwarecomponents of the four operational subsystems.

TABLE 1 Subsystem Laser Camera 1A 1 1 1B 2 1 2A 2 2 2B 1 2

Together, the four subsystems offer differing operationalcharacteristics that the controlling software may call upon during agiven measurement cycle. For example, subsystems 1A and 2A behave as theexisting overhead dimensioning system but with differing fields of view.When operating together, for an object positioned centrally below, theyare able to reduce the problem of laser and camera occlusion. Theaccuracy of subsystems 1A and 2A can be improved across the field ofview by the addition of the lookup table calibration approach.Alternatively, subsystems 1B and 2B have a much greater baselineseparation and are thus able to offer significantly improved accuracy ofheight determination, although at the cost of increased laser occlusion.

It can be observed that the determination of the object's maximum heightdoes not suffer from the problem of occlusion; therefore, subsystems 1Band 2B are able to provide increased accuracy for this purpose. On theother hand, subsystems 1A and 2A have the ability to recover occludedareas and thereby improve accuracy in the determination of the object'slength and breadth. Thus, the subsystems offer a hybrid approach to thedimensioning system.

Generally, objects to be dimensioned are nominally placed on a floormark, i.e., measurement space, located centrally between the twocameras. The central placement reduces occlusion issues, althoughobjects located between and at the periphery of both camera fields ofview can be disadvantageous due to radial lens distortion, with anyregistration errors being more significant.

The dimensioning process begins by scanning laser 1 rapidly through themeasurement space. During the rapid scan, cameras 1 and 2 determine theapproximate location and extent of the object. Laser 1 is scanned overthe object and cameras 1 and 2 (subsystems 1A and 2B) acquire pointcloud data simultaneously. Laser 2 is scanned over the object andcameras 1 and 2 (subsystems 1B and 2A) acquire point cloud datasimultaneously. The point cloud data acquired by the subsystems ismerged and fit in a cuboid. It is to be understood that the acquisitionof point cloud data can be attained by multiplexing these steps to gaina speed advantage. Furthermore, it may also be possible to apply atransformation when merging the cloud data to accommodate anymisregistration.

To combat accuracy errors, e.g., distortion or misregistration, arisingfrom objects placed between the cameras near the periphery and betweenthe two fields of view, the configuration shown in FIG. 33 can bearranged as shown in FIG. 34 wherein the cameras are pointed toward thecentral object location. In this configuration, it is necessary toperform transformations upon the acquired point cloud data to map dataacquired in the local coordinate frames to a common world coordinateframe. However, to provide the same combined field of view (with reducedocclusion) as obtained with parallel optical axes, the camera spacingshould be increased. Also, to avoid a reduction in accuracy caused by areduction in camera-to-laser separation, the lasers can be furtherseparated, although this may result in a fall-off of reflectedintensity. Because both cameras have a view of much of the object, astereo vision approach can be incorporated.

Another embodiment of the present invention shown in FIG. 36 cansignificantly reduce occlusion by utilizing a four camera-laser systemsetup configured in 90° increments. A less costly configuration is shownin FIG. 35 and incorporates a three camera-laser system setup arrangedin 120° increments with the local data mapped to a world coordinateframe.

While the specific embodiments have been illustrated and described,numerous modifications come to mind without significantly departing fromthe spirit of the invention, and the scope of protection is only limitedby the scope of the accompanying claims.

1. A method for determining the dimensions of at least one item, themethod comprising: determining an approximate location and extent of atleast one item; acquiring a first set of point cloud data by utilizing afirst laser to transmit a first signal over the at least one item andutilizing a first camera to receive a reflection of the first signal;utilizing a second laser and a second camera to determine an approximatelocation and a dimension of the at least one item; acquiring a secondset of point cloud data by utilizing the second laser to transmit asecond signal over the at least one item and utilizing the second camerato receive a reflection of the second signal; acquiring a third set ofpoint cloud data by utilizing the second camera to receive thereflection of the first signal; constructing a three-dimensional imagethat defines the at least one item by merging the acquired first, secondand third sets of point cloud data; and determining a rectangular prismhaving a height, length and breadth to contain the constructed imagedefining the at least one item to ascertain the rectangular prism withinwhich the at least one item will fit.
 2. The method of claim 1, furthercomprising: acquiring a fourth set of point cloud data by utilizing thefirst camera to receive the reflection of the second signal; andconstructing the three-dimensional image that defines the at least oneitem by merging the acquired first, second, third, and fourth sets ofpoint cloud data.
 3. The method of claim 1, further comprising:transforming the constructed image to a global coordinate system.
 4. Amethod for determining the dimensions of at least one item, the methodcomprising: determining an approximate location and extent of at leastone item; acquiring a first set of point cloud data by utilizing a firstlaser to transmit a first signal over the at least one item andutilizing a first camera to receive a reflection of the first signal;constructing a three-dimensional image that defines the at least oneitem from the acquired first set of point cloud data; compensating forlens distortion of the constructed three-dimensional image comprisingutilizing a pixel point correction value in cooperation with theacquired first set of point cloud data to adjust a location of eachpixel point affected by radial lens distortion, the pixel pointcorrection value being developed at least in part by: providing a pixelvalue for a pixel within the measurement space; acquiring a scannedpixel value by utilizing the first laser to transmit the first signalover the measurement space and utilizing the first camera to receive areflection off a pixel of the first signal; comparing the pixel valuewith the scanned pixel value; and generating a pixel correction value inresponse to the comparison; and determining a rectangular prism having aheight, length, width and breadth and, as compensated for lensdistortion, to contain the constructed image defining the at least oneitem to ascertain the rectangular prism within which the at least oneitem will fit.
 5. The method of claim 4, further comprising: storing thepixel correction value in a calibration lookup table, wherein the pixelcorrection value can be utilized during construction of thethree-dimensional image.
 6. The method of claim 4, further comprising:utilizing the pixel correction value to generate an equation forcorrecting distortions.
 7. A method for determining the dimensions of atleast one item, the method comprising: determining an approximatelocation and extent of at least one item; acquiring a first set of pointcloud data by utilizing a first laser to transmit a first signal overthe at least one item and utilizing a first camera to receive areflection of the first signal; constructing a three-dimensional imagethat defines the at least one item from the acquired first set of pointcloud data; reducing noise from the image utilizing image subtractionby: acquiring a first image that represents the at least one item byutilizing the first laser to transmit the first signal over themeasurement space and utilizing the first camera to receive thereflection of the first signal; acquiring a second image that representsthe at least one item by utilizing the first laser to transmit the firstsignal over the measurement space and utilizing the first camera toreceive the reflection of the first signal; subtracting the second imagefrom the first image to produce a gray-level image; and utilizing thegray-level image as a threshold value for providing a binary image; anddetermining a rectangular prism having a height, length and breadth tocontain the constructed image, after noise reduction therefrom, definingthe at least one item to ascertain the rectangular prism within whichthe at least one item will fit.
 8. The method of claim 7, whereinreducing noise comprises: determining a median pixel value for apredetermined area surrounding a pixel; and setting each pixel to itsrespective median pixel value.
 9. A method for determining thedimensions of at least one item, the method comprising: determining anapproximate location and extent of at least one item; acquiring a firstset of point cloud data by utilizing a first laser to transmit a firstsignal over the at least one item and utilizing a first camera toreceive a reflection of the first signal; constructing athree-dimensional image that defines the at least one item from theacquired first set of point cloud data; reducing noise from the image,comprising: computing a spatial histogram of the point cloud data in avertical direction; computing a spatial histogram of the point clouddata in a horizontal direction; grouping points having a spatiallydetached value; comparing an amount of points in a grouping against apredetermined value; identifying each grouping having a lesser amount ofpoints than the predetermined value; and removing each identifiedgrouping; and determining a rectangular prism having a height, lengthand breadth to contain the constructed image, after noise reductiontherefrom, defining the at least one item to ascertain the rectangularprism within which the at least one item will fit.
 10. The method ofclaim 9, wherein reducing noise further comprises: computing thevertical spatial histogram from rotation of the point cloud data in anx-plane; and computing the horizontal spatial histogram from rotation ofthe point cloud data in a y-plane.
 11. A method for determining thedimensions of at least one item, the method comprising: determining anapproximate location and extent of at least one item; acquiring a firstset of point cloud data by utilizing a first laser to transmit a firstsignal over the at least one item and utilizing a first camera toreceive a reflection of the first signal; constructing athree-dimensional image that defines the at least one item from theacquired first set of point cloud data; reducing noise from the image,comprising: identifying points in a point cloud, each point having aheight; grouping the points by the height of each point; comparing anamount of points in each grouping against a predetermined value;identifying each grouping having a lesser amount of points than thepredetermined value; and removing each identified grouping; anddetermining a rectangular prism having a height, length and breadth tocontain the constructed image, after noise reduction therefrom, definingthe at least one item to ascertain the rectangular prism within whichthe at least one item will fit.
 12. A method for determining thedimensions of at least one item, the method comprising: determining anapproximate location and extent of at least one item; acquiring a firstset of point cloud data by utilizing a first laser to transmit a firstsignal over the at least one item and utilizing a first camera toreceive a reflection of the first signal; constructing athree-dimensional image that defines the at least one item from theacquired first set of point cloud data; reducing noise from the image,comprising: identifying a position of each disjoint point in ameasurement array; comparing a height value of each disjoint pointagainst a height value of a surrounding signal; and removing eachdisjoint point not matching the height value of the surrounding signal;and determining a rectangular prism having a height, length and breadthto contain the constructed image, after noise reduction therefrom,defining the at least one item to ascertain the rectangular prism withinwhich the at least one item will fit.
 13. A method for determining thedimensions of at least one item place within a measurement space, themethod comprising: determining an approximate location and extent of atleast one item; acquiring a first set of point cloud data by utilizing afirst laser to transmit a first signal over the at least one item andutilizing a first camera to receive a reflection of the first signal;constructing a three-dimensional image that defines the at least oneitem from the acquired first set of point cloud data including utilizinga point threshold in cooperation with the image during construction ofthe image; and determining a rectangular prism having a height, lengthand breadth to contain the constructed image defining the at least oneitem to ascertain the rectangular prism within which the at least oneitem will fit.
 14. The method of claim 13, further comprising:identifying a gray-scale value for each acquired point; utilizing eachidentified point to determine a statistical property of the gray-scalevalue; and defining the point threshold in response to the determinedstatistical property of the gray-scale value.
 15. The method of claim13, further comprising: providing a group of point threshold values fromwhich to select the point threshold.
 16. A method for determining thedimensions of at least one item, the method comprising: determining anapproximate location and extent of at least one item; acquiring a firstset of point cloud data by utilizing a first laser to transmit a firstsignal over the at least one item and utilizing a first camera toreceive a reflection of the first signal; constructing athree-dimensional image that defines the at least one item from theacquired first set of point cloud data; and determining a rectangularprism to contain the constructed image, the rectangular prism having aheight, length, and breadth, wherein determining comprises: determiningdimensions of the rectangular prism by rotating a coordinate frame abouta centroid of the constructed image through a plurality of angularincrements; measuring a distance from the centroid to an edge of the atleast one item for each angular increment; storing each measurement;identifying a length measurement and a breadth measurement; andselecting a single length measurement and a single breadth measurement,wherein the selected measurements, in combination with a determinedheight of the at least one item, comprise dimensions of a rectangularprism having the smallest volume which would contain the at least oneitem.
 17. A method for determining the dimensions of at least one item,the method comprising: determining an approximate location and extent ofat least one item; acquiring a first set of point cloud data byutilizing a first laser to transmit a first signal over the at least oneitem and utilizing a first camera to receive a reflection of the firstsignal, wherein acquiring a first set of point cloud data comprises:coarsely transmitting the first signal in a first direction at anoff-center location within the measurement space; identifying a firstedge of the at least one item; finely transmitting the first signal in asecond direction over the first edge, the second direction beingopposite the first direction; coarsely transmitting the first signal inthe second direction at the off-center location within the measurementspace; identifying a second edge of the at least one item; and finelytransmitting the first signal in the first direction over the secondedge; constructing a three-dimensional image that defines the at leastone item from the acquired first set of point cloud data; anddetermining a rectangular prism having a height, length and breadth tocontain the constructed image defining the at least one item toascertain the rectangular prism within which the at least one item willfit.
 18. A system for determining the dimensions of at least one itemset within a measurement space, the system comprising: a first laserlocated and oriented for transmitting a first signal through ameasurement space within which at least one item may reside, the firstlaser having a coarse transmission mode and a fine transmission mode; afirst camera located and oriented for receiving the first signal andacquiring a plurality of data points comprising a first set of pointcloud data from reflections of the first signal from the at least oneitem; an array generator for constructing an image from the acquiredfirst set of point cloud data; and a rectangular prism generator forconstructing a rectangular prism in response to dimensions of theconstructed image.
 19. The system of claim 18, further comprising: alens distortion compensator for compensating for lens distortion of theconstructed image.
 20. The system of claim 19, wherein the lensdistortion compensator is configured to determine an image pointcorrection factor during calibration of the system for use incooperation with the acquired first set of point cloud data to adjust alocation of each image point affected by radial lens distortion.
 21. Thesystem of claim 18, further comprising: a noise filter.
 22. The systemof claim 21, wherein the noise filter is configured to determine: amedian pixel value by an area surrounding a pixel; and furthercomprising a designator for setting each pixel to its respective medianpixel value.
 23. The system of claim 21, wherein the noise filter isconfigured to generate: a vertical spatial histogram of the acquiredfirst set of cloud point data from rotation of the acquired first set ofpoint cloud data in a vertical direction; a horizontal spatial histogramof the acquired first set of point cloud data from rotation of theacquired first set of point cloud data in a horizontal direction; andfurther comprising a grouper for grouping points having a spatiallydetached value, wherein each group having a lesser amount of points thana predetermined value is removed from the image.
 24. The system of claim21, wherein the noise filter comprises: an identifier for identifyingpoints in a point cloud, each point having a height; a grouper forgrouping the points by the height of each point; and a comparator forcomparing an amount of points in each group against a predeterminedvalue, wherein each group having a lesser amount of points than apredetermined value is removed.
 25. The system of claim 21, wherein thenoise filter comprises: an identifier for identifying a position of eachdisjoint point in a measurement image; and a comparator for comparing aheight value of each disjoint point against a height value of asurrounding signal; wherein each disjoint point not matching the heightvalue of the surrounding signal is removed.
 26. The system of claim 18,further comprising: means for determining a point threshold by:identifying a gray-scale value for each point found in an image;utilizing each identified point to determine a statistical property ofthe gray-scale value; and selecting the point threshold in response tothe determined statistical property of the gray-scale value.
 27. Thesystem of claim 26, further comprising: means for generating a group ofpoint threshold values from which to select the point threshold inresponse to calibration of the system.
 28. The system of claim 18,further comprising: a second laser located and oriented for transmittinga second signal through the measurement space, the second laser having acoarse transmission mode and a fine transmission mode; a second cameralocated and oriented for receiving the second signal and acquiring aplurality of data points comprising a second set of point cloud datafrom reflections of the second signal from the at least one item; andwherein the array generator is configured to utilize the acquired firstand second sets of point cloud data to construct the image.
 29. Thesystem of claim 28, wherein the second camera is located and orientedfor acquiring a plurality of data points comprising a third set of pointcloud data from reflections of the first signal from the at least oneitem and wherein the array generator is configured to utilize theacquired first, second, and third sets of point cloud data to constructthe image.
 30. The system of claim 29, wherein the first camera islocated and oriented for acquiring a plurality of data points comprisinga fourth set of point cloud data from reflections of the second signalfrom the at least one item and wherein the array generator is configuredto utilize the acquired first, second, third, and fourth sets of pointcloud data to construct the image.
 31. The system of claim 30, furthercomprising: a third laser located and oriented for transmitting a thirdsignal through the measurement space, the third laser having a coarsetransmission mode and a fine transmission mode; a third camera locatedand oriented for receiving the third signal and acquiring a plurality ofdata points comprising a fifth set of point cloud data from reflectionsof the third signal from the at least one item and wherein the arraygenerator is configured to utilize the acquired first, second, third,fourth, and fifth sets of point cloud data to construct the image. 32.The system of claim 31, wherein the third camera is located and orientedfor acquiring a plurality of data points comprising a sixth set of pointcloud data from reflections of the first signal from the at least oneitem and wherein the array generator is configured to utilize theacquired first, second, third, fourth, fifth, and sixth sets of pointcloud data to construct the image.
 33. The system of claim 32, whereinthe third camera is located and oriented for acquiring a plurality ofdata points comprising a seventh set of point cloud data fromreflections of the second signal from the at least one item and whereinthe array generator is configured to utilize the acquired first, second,third, fourth, fifth, sixth, and seventh sets of point cloud data toconstruct the image.
 34. The system of claim 33, wherein the firstcamera is located and oriented for acquiring a plurality of data pointscomprising an eighth set of point cloud data from reflections of thethird signal from the at least one item and wherein the array generatoris configured to utilize the acquired first, second, third, fourth,fifth, sixth, seventh, and eighth sets of point cloud data to constructthe image.
 35. The system of claim 34, wherein the second camera islocated and configured for acquiring a plurality of data pointscomprising a ninth set of point cloud data from reflections of the thirdsignal from the at least one item and wherein the array generator isconfigured to utilize the acquired first, second, third, fourth, fifth,sixth, seventh, eighth, and ninth sets of point cloud data to constructthe image.
 36. The system of claim 31, wherein the first, second, andthird cameras lie on a first perimeter and the first, second, and thirdlasers lie on a second perimeter.
 37. The system of claim 36, whereinthe first, second, and third cameras are spaced 120° about the center ofthe first perimeter, and the first, second, and third lasers are spaced120° about the center of the second perimeter.
 38. The system of claim37, wherein the first and second perimeters are concentric circles,respectively, the first circle being contained within the second circle.39. The system of claim 28, wherein the first camera and the first laserlie on a first axis and the second camera and the second laser lie on asecond axis.
 40. The system of claim 39, wherein the first and secondaxes are parallel.
 41. The system of claim 40, wherein both the firstcamera and the second camera are located between the first laser and thesecond laser.
 42. A computer-readable medium having an applicationtherein to facilitate dimensioning of at least one item, the mediumcomprising: a segment for determining an approximate location and extentof at least one item; a segment for acquiring a first set of point clouddata by utilizing a first laser to transmit a first signal over the atleast one item and utilizing a first camera to receive a reflection ofthe first signal from the at least one item; a segment for utilizing asecond laser and a second camera to determine the approximate locationand a dimension of the at least one item; a segment for acquiring asecond set of point cloud data by utilizing the second laser to transmita second signal over the at least one item and utilizing the secondcamera to receive a reflection of the second signal from the at leastone item; a segment for acquiring a third set of point cloud data byutilizing the second camera to receive the reflection of the firstsignal from the at least one item; a segment for constructing an imagedefining the at least one item by merging the acquired first, second,and third sets of point cloud data; and a segment for determining arectangular prism having a height, length and breadth to contain aconstructed image defining the at least one item to ascertain therectangular prism within which the at least one item will fit.
 43. Acomputer-readable medium having an application therein to facilitatedimensioning of at least one item, the medium comprising: a segment fordetermining an approximate location and extent of at least one item; asegment for acquiring a first set of point cloud data by utilizing afirst laser to transmit a first signal over the at least one item andutilizing a first camera to receive a reflection of the first signalfrom the at least one item; a segment for constructing an image definingthe at least one item from the acquired first set of point cloud data; asegment for reducing noise from the constructed image, comprising: asegment for identifying points in a point cloud, each point having aheight; a segment for grouping the points by the height of each point; asegment for comparing an amount of points in each grouping against apredetermined value; a segment for identifying each grouping having alesser amount of points than the predetermined value; and a segment forremoving each identified grouping; and a segment for determining arectangular prism having a height, length and breadth to contain aconstructed image, after noise reduction therefrom, defining the atleast one item to ascertain the rectangular prism within which the atleast one item will fit.
 44. The medium of claim 42, further comprising:a segment for compensating for lens distortion of the constructed image.45. The medium of claim 44, wherein the segment for compensating forlens distortion comprises: a segment for utilizing an image pointcorrection factor in cooperation with the acquired first set of pointcloud data to adjust a location of each image point affected by radiallens distortion.
 46. The medium of claim 45, further comprising: asegment for storing the image point correction factor in a calibrationlookup table, wherein the image point correction factor is associatedwith an image point location.
 47. The medium of claim 42, furthercomprising: a segment for utilizing a point threshold duringconstruction of the image.
 48. The medium of claim 47, furthercomprising: a segment for identifying a gray-scale value for eachacquired point; a segment for utilizing each identified point todetermine a statistical property of the gray-scale value; and a segmentfor defining the point threshold in response to the determinedstatistical property of the gray-scale value.
 49. The medium of claim47, further comprising: a segment for providing a group of pointthreshold values from which to select the point threshold.
 50. Themedium of claim 42, further comprising: a segment for transforming theconstructed image to a global coordinate system.
 51. A computer-readablemedium having an application therein to facilitate dimensioning of atleast one item, the medium comprising: a segment for determining anapproximate location and extent of at least one item; a segment foracquiring a first set of point cloud data by utilizing a first laser totransmit a first signal over the at least one item and utilizing a firstcamera to receive a reflection of the first signal from the at least oneitem; a segment for constructing an image defining the at least one itemfrom the acquired first set of point cloud data; a segment for reducingnoise from the constructed image, comprising: a segment for computing aspatial histogram of the point cloud data in a vertical direction; asegment for computing a spatial histogram of the point cloud data in ahorizontal direction; a segment for grouping points having a spatiallydetached value; a segment for comparing an amount of points in agrouping against a predetermined value; a segment for identifying eachgrouping having a lesser amount of points than the predetermined value;and a segment for removing each identified grouping; and a segment fordetermining a rectangular prism having a height, length and breadth tocontain a constructed image, after noise reduction therefrom, definingthe at least one item to ascertain the rectangular prism within whichthe at last one item will fit.
 52. The medium of claim 51, wherein thesegment for reducing noise comprises: a segment for determining a medianpixel value for a predetermined area surrounding a pixel; and a segmentfor setting each pixel to its respective median pixel value.
 53. Themedium of claim 42, further comprising: a segment for acquiring a fourthset of point cloud data by utilizing the first camera to receive thereflection of the second signal from the at least one item; and asegment for constructing the image by merging the acquired first,second, third, and fourth sets of point cloud data.
 54. Acomputer-readable medium having an application therein to facilitatedimensioning of at least one item, the medium comprising: a segment fordetermining an approximate location and extent of at least one item; asegment for acquiring a first set of point cloud data by utilizing afirst laser to transmit a first signal over the at least one item andutilizing a first camera to receive a reflection of the first signalfrom the at least one item; a segment for constructing an image of theat least one item from the acquired first set of point cloud data; asegment for reducing noise from the constructed image, comprising: asegment for identifying a position of each disjoint point in ameasurement array; a segment for comparing a height value of eachdisjoint point against a height value of a surrounding signal; and asegment for removing each disjoint point not matching the height valueof the surrounding signal; and a segment for determining a rectangularprism having a height, length and breadth to contain a constructedimage, after noise reduction therefrom, of the at least one item toascertain the rectangular prism within which the at least one item willfit.
 55. A computer-readable medium having an application therein tofacilitate dimensioning of at least one item, the medium comprising: asegment for determining an approximate location and extent of at leastone item; a segment for acquiring a first set of point cloud data byutilizing a first laser to transmit a first signal over the at least oneitem and utilizing a first camera to receive a reflection of the firstsignal from the at least one item; a segment for constructing an imageof the at least one item from the acquired first set of point clouddata; a segment for determining a rectangular prism having a height,length and breadth to contain a constructed image; a segment fordetermining the dimensions of the rectangular prism by rotating acoordinate frame about the centroid of the constructed image through aplurality of angular increments; and a segment for measuring a distancefrom the centroid to an edge of the constructed image for each angularincrement; a segment for storing each measurement; a segment foridentifying a length measurement and a breadth measurement; and asegment for selecting a single length measurement and a single breadthmeasurement, wherein the selected measurements, in combination with adetermined height of the at least one item, comprise dimensions of arectangular prism having the smallest volume which would contain the atleast one item.
 56. A computer-readable medium having an applicationtherein to facilitate dimensioning of at least one item, the mediumcomprising: a segment for determining an approximate location and extentof at least one item; a segment for acquiring a first set of point clouddata by utilizing a first laser to transmit a first signal over the atleast one item and utilizing a first camera to receive a reflection ofthe first signal from the at least one item, wherein the segment foracquiring a first set of point cloud data comprises: a segment forcoarsely transmitting the first signal in a first direction at anoff-center location within the measurement space; a segment foridentifying a first edge of the at least one item; a segment for finelytransmitting the first signal in a second direction over the first edge,the second direction being opposite the first direction; a segment forcoarsely transmitting the first signal in the second direction at theoff-center location within the measurement space; a segment foridentifying a second edge of the at least one item; and a segment forfinely transmitting the first signal in the first direction over thesecond edge; a segment for constructing an image defining the at leastone item from the acquired first set of point cloud data; and a segmentfor determining a rectangular prism having a height, length and breadthto contain a constructed image defining the at least one item toascertain the rectangular prism within which the at least one item willfit.